Lecture 8: Data Visualization and EDA

• library(ggplot), author Hadley Wickham. First release on June 10, 2007.
• gg stands for “Grammar of Graphics”

https://qz.com/1007328/all-hail-ggplot2-the-code-powering-all-those-excellent-charts-is-10-years-old/

ggplot2 Examples

Why do we need ggplot?

It’s part of the exploration of the data via visualization.

ggplot system

# library(ggplot2)
ggplot(airquality, aes(Temp, Ozone)) + 
        geom_point() + 
        geom_smooth(method = "loess", se = FALSE)
## Warning: Removed 37 rows containing non-finite values (stat_smooth).
## Warning: Removed 37 rows containing missing values (geom_point).

Syntax of ggplot

• Definition of data + Definitions of layers

  ggplot(data = <DATA>, ...) + 
    <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

Syntax of ggplot

• For example, geom_point() is for points, geom_line() is for line, geom_smooth() for smoothed line.
• The definition of data will pass down to the layers. But layers can have its own data.

• Put the + sign in the end of the line, not the beginning of the line.

  ggplot(data = d1, ...) +
    geom_point() + # this would get data = d1
    geom_point(data = d2, ...) # this would get data = d2

• Below would result in error

  ggplot(data = <DATA>, ...)
  + <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

Simply plot x and y

ggplot(bank, aes(age, balance)) + geom_point()

aes and aes_string

If you don’t know the column name, use aes_string to pass variable name as string/character.

ggplot(bank, aes_string("age", "balance", color = "job")) + geom_point()

Example of aes_string

# in a Shiny app
selectInput("column", "col", choices = c("balance", "duration"))
plot_bank("age", input$col)

For non-numeric data: default and age

ggplot(bank, aes(default, age)) + geom_point()
ggplot(bank, aes(age, default)) + geom_point()
ggplot(bank, aes(job, age)) + geom_point()

ggplot(bank, aes(default, age)) + geom_point()

For non-numeric data: age and default (make it landscape)

ggplot(bank, aes(age, default)) + geom_point()

For non-numeric data: job and age

ggplot(bank, aes(job, age)) + geom_point()

Add 2nd geometry

ggplot(bank, aes(age, balance)) + geom_point() + geom_smooth()
ggplot(bank, aes(age, balance, color = job)) + geom_point() + geom_smooth()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Pass aes down

geom_* functions has a default parameter of inherit.aes = T.

ggplot(bank, aes(x = age, y = duration)) +
geom_smooth() + # same as geom_smooth(aes(x = age, y = duration))
geom_point() # same as geom_point(aes(x = age, y = duration))
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Pass aes down

# This is equivalent to below. But this is a bit repeating.
ggplot(bank) +
geom_point(aes(x = age, y = duration)) +
geom_smooth(aes(x = age, y = duration))
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Pass aes down

# But repeating is useful sometimes.
# we can do specify different data and aes for different geom_* functions.
ggplot(bank) +
geom_point(aes(x = age, y = duration)) +
geom_smooth(data = dplyr::filter(bank, job == "entrepreneur"),
            aes(x = age, y = duration), color = "green") +
geom_smooth(data = dplyr::filter(bank, job == "blue-collar"),
            aes(x = age, y = duration), color = "blue") +
geom_smooth(data = dplyr::filter(bank, job != "entrepreneur"),
            aes(x = age, y = duration), color = "orange")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Adjustment: legend bottom

# adjust legend position
ggplot(bank, aes(x = age, y = duration, color = job)) +
geom_point() +
theme(legend.position="bottom")

Adjustment: legend left

# legend to the left
ggplot(bank, aes(x = age, y = duration, color = job)) +
geom_point() +
theme(legend.position="left")

Adjustment: coordinate flip

# Flip the x and y axis
# Different feeling?
ggplot(bank, aes(x = age, y = duration, color = job)) +
geom_point() +
theme(legend.position="left") + 
coord_flip()

Adjustment: log scale

# Make y as log scaled.
# Note that before flip, x is y, so we use scale_y_log10()
ggplot(bank, aes(x = age, y = duration, color = job)) +
geom_point() +
theme(legend.position="left") + 
coord_flip() +
scale_y_log10()

Each + is a layer

# Nearly empty chart.
g <- ggplot(bank, aes(x = age, y = duration))
# This is almost empty
g <- ggplot(bank)
# This is really empty.
g <- ggplot()

Combine g with layers

ggplot(bank, aes(x = age, y = duration)) + 
  geom_point() + geom_smooth()

# This is equivalent to above
g <- ggplot(bank, aes(x = age, y = duration))
g + geom_point() + geom_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

g can be re-used.

• g can be re-used. It’s good to be used when we want to exploratory data and try to plot many figures.
• Fixed a few variables in g <- ggplot(data, aes(...)).

• Use g + geom_XXX() to find the best representation for the relationship.

  g + geom_point() + geom_smooth(method = "lm") + facet_grid(. ~ job)
  g + geom_point(color = "steelblue", size = 4, alpha = 1/2)
  g + geom_point(aes(color = job), size = 4, alpha = 1/2)
  g + geom_point() + geom_point(aes(color = job), size = 4, alpha = 1/2) 

g: mix and match: which figure?

g + geom_point() + geom_smooth(method = "lm") + facet_grid(. ~ job)
g + geom_point(color = "steelblue", size = 4, alpha = 1/2)
g + geom_point(aes(color = job), size = 4, alpha = 1/2)
g + geom_point() + geom_point(aes(color = job), size = 4, alpha = 1/2) 

g: mix and match: which figure?

g + geom_point() + geom_smooth(method = "lm") + facet_grid(. ~ job)
g + geom_point(color = "steelblue", size = 4, alpha = 1/2)
g + geom_point(aes(color = job), size = 4, alpha = 1/2)
g + geom_point() + geom_point(aes(color = job), size = 4, alpha = 1/2) 

g: mix and match: which figure?

g + geom_point() + geom_smooth(method = "lm") + facet_grid(. ~ job)
g + geom_point(color = "steelblue", size = 4, alpha = 1/2)
g + geom_point(aes(color = job), size = 4, alpha = 1/2)
g + geom_point() + geom_point(aes(color = job), size = 4, alpha = 1/2) 

g: mix and match: which figure?

g + geom_point() + geom_smooth(method = "lm") + facet_grid(. ~ job)
g + geom_point(color = "steelblue", size = 4, alpha = 1/2)
g + geom_point(aes(color = job), size = 4, alpha = 1/2)
g + geom_point() + geom_point(aes(color = job), size = 4, alpha = 1/2) 

Differentiate groups - shape

# Use shape
ggplot(bank) +
  geom_point(aes(age, duration, shape = contact))

Differentiate groups - color

# Use color
ggplot(bank) +
  geom_point(aes(age, duration, color = contact))

Differentiate groups - size

# Use size
ggplot(bank) +
  geom_point(aes(age, duration, size = contact))
## Warning: Using size for a discrete variable is not advised.

Differentiate groups - alpha

# Use alpha - transparency
ggplot(bank) +
  geom_point(aes(age, duration, alpha = contact))
## Warning: Using alpha for a discrete variable is not advised.

Differentiate groups - group

# Use group.
ggplot(bank) +
  geom_point(aes(age, duration, group = contact))

Enforce color, put things outside aes

## you can also enforce color, put things outside aes
ggplot(bank) +
  geom_point(aes(age, duration), color = "blue", size = 10, alpha = 0.4)

What’s inside bank’s clients? Things to consider

• Which variables in data are categorical?

• Which variables are continuous?

• bio:
  • age
  • job
  • marital
  • education
• financial
  • default
  • balance
  • housing
  • loan

What’s inside bank’s clients? Things to consider 2

• communication
  • contact: celluar v.s. telephone v.s. unknown
  • day/month: maybe good to ignore?
  • duration:
  • campaign:
  • pdays:
  • previous:
  • poutcome:

Categorical/Continuous variable

• numeric -> continuous data
• string -> discrete data

For different type of variables, the scale is also different

• Reverse a categorical (discrete) variable, we use scale_y_discrete(limit = rev(levels(...))).
• Reverse a continuous numerical variable, we use scale_x_reverse().

Exercise

1. What does this figure mean?

ggplot(bank, aes(age, job)) +
geom_point() +
scale_y_discrete(limit = rev(levels(bank$job)))

Exercise

# y labels without sort.
ggplot(bank, aes(age, job)) + geom_point()

Exercise

2. I tried to plot between job, loan and age. Any better idea?

suppressWarnings(
  ggplot(bank, aes(age, job, color = loan)) + geom_point()
)

## Warning: Using size for a discrete variable is not advised.

Other geoms

• geom_boxplot()
• geom_density()
• geom_histogram()
• geom_bar()

Boxplot: job and duration

ggplot(bank, aes(job, duration)) + geom_boxplot()

Boxplot: job and age

ggplot(bank, aes(job, age)) + geom_boxplot()

Density: balance and job

ggplot(bank, aes(balance, color = job)) + geom_density()

Density: balance and job with fill

ggplot(bank, aes(duration, fill = job)) + geom_density()

Density: age and job with alpha

ggplot(bank, aes(age, color = job, alpha = 0.3)) + geom_density()

Density: age and job with alpha and fill and color

# Which is better?
ggplot(bank, aes(age, color = job, fill = job, alpha = 0.3)) + geom_density()

histogram: duration and job and binwidth = 2

ggplot(data = bank, mapping = aes(x = duration, fill = job)) + geom_histogram(binwidth = 2)

histogram: duration and job and binwidth = 100

ggplot(data = bank, mapping = aes(x = duration, fill = job)) + geom_histogram(binwidth = 100)

histogram: age and job and fill

ggplot(data = bank, mapping = aes(x = age, fill = job)) + geom_histogram(binwidth = 10)

histogram: age and job and colour

ggplot(data = bank, mapping = aes(x = age, colour = job)) + geom_freqpoly(binwidth = 10)

geom_bar

• bar is a statistical function: It counts.

# First input parameter to geom_bar is mapping, so we can skip it.
ggplot(bank) + geom_bar(mapping = aes(x = age))

# We can skip mapping
ggplot(bank) + geom_bar(aes(x = age))

geom_bar with fill: job

# comparing to colour, for Bar, we better use fill
# ggplot(data = bank, ) + geom_bar(aes(x = age, colour = job))
ggplot(bank) + geom_bar(mapping = aes(x = age, fill = job))

geom_bar with fill: age

ggplot(bank) +
  geom_bar(mapping = aes(x = job))
# Color doesn't work, because age is a continous variable.
ggplot(bank) +
  geom_bar(mapping = aes(x = job, fill = age)) 

Position for bar

ggplot(bank) + geom_bar(mapping = aes(x = age, fill = job))

Position for bar: fill

# fill to 100%
ggplot(bank) + geom_bar(mapping = aes(x = age, fill = job),
                        position = "fill")

Position for bar: dodge

# dodge means "adaptive width of the bar"
ggplot(bank) + geom_bar(mapping = aes(x = age, fill = job),
                        position = "dodge")

Variations: coord_flip

# Switch x and y axis.
# Note any adjustment on x or y axis is effective on the original name.
ggplot(bank) +
geom_bar(mapping = aes(x = age, fill = job), position = "fill") +
coord_flip()

Variations: coord_polar

ggplot(bank) +
geom_bar(mapping = aes(x = age, fill = job), position = "fill") +
coord_polar()

Variations: scale_x_reverse

# scale_x_reverse works on continous variable (numeric, date, etc.). Make it big to small.
ggplot(bank) +
geom_bar(mapping = aes(x = age, fill = job), position = "fill") +
coord_flip() +
scale_x_reverse()

geom_bar: better serves for categorical data

ggplot(data = bank, mapping = aes(x = job, fill = education)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

geom_bar: coord_flip

ggplot(data = bank, mapping = aes(x = job, fill = education)) +
  geom_bar() + coord_flip()

geom_bar: sort job by mean age

ggplot(data = bank, mapping = aes(x = reorder(job, age, FUN = mean),
       fill = education)) +
  geom_bar() + coord_flip()

geom_bar: sort job by alphabetical order

# If we want to order job according to alphabetical order.
# use rev(levels(...))
ggplot(data = bank, mapping = aes(x = reorder(job, age, FUN = median),
       fill = education)) +
  geom_bar() +
  scale_x_discrete(limit = rev(levels(bank$job))) +
  coord_flip()

Bar with composite data

ggplot(data = bank, mapping = aes(x = reorder(job, age, FUN = median),
       fill = education)) +
  # layer 1
  geom_bar() +
  # If we want to sort the job acccording to median age
  scale_x_discrete(limit =
    rev(levels(reorder(bank$job, bank$age, FUN = median)))) +
  # And also add age range and median age.
  geom_line(aes(x = job, y = age)) +
  geom_point(data = group_by(bank, job) %>%
               summarize(age = median(age)) %>% ungroup,
            aes(x = job, y = age), inherit.aes = FALSE) +
  xlab("Job sorted according to\nMedian age\n(Top - younger)") +
  coord_flip()

Bar with composite data: plot

Data with statistical

ggplot(data = bank) + 
  stat_summary(
    mapping = aes(x = age, y = balance),
    fun.ymin = min,
    fun.ymax = max,
    fun.y = median
  )

Facets

• Create invidual figures.
• facet_grid: basic
• facet_wrap: you can control number of rows and cols

ggplot(data = bank) + 
  geom_point(mapping = aes(x = age, y = duration)) + 
  facet_grid( ~ education)

Facets - facet_wrap

ggplot(data = bank) + 
  geom_point(mapping = aes(x = age, y = duration)) + 
  facet_wrap(~ education, nrow = 2)

Facets - facet_wrap multi-dimension

ggplot(data = bank) + 
  geom_point(mapping = aes(x = age, y = duration)) + 
  facet_wrap(loan ~ education ~ housing, nrow = 2)

  # or we can use, facet_grid(loan ~ education ~ housing)

Facets - finding the best

# doesn't look great because we have so many jobs.
ggplot(bank, aes(pdays)) + geom_histogram() + facet_grid(job ~ .)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Facets - finding the best. still trying.

# Not a good choice, neither
ggplot(bank, aes(pdays)) + geom_histogram() + facet_grid(. ~ job)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Facets - finding the best. better

# Can we do better?
ggplot(bank, aes(campaign)) + geom_histogram() + facet_grid(. ~ job)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Facets - finding the best. try another

ggplot(bank, aes(duration)) + geom_histogram(aes(color = job)) +
  facet_grid(. ~ job)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Facets - finding the best. Try points

# facet with points is good
ggplot(bank, aes(balance, age)) + geom_point() + facet_grid(. ~ job)

Facets - finding the best. Better with color

# do better
ggplot(bank, aes(balance, age)) + geom_point(aes(color = job)) +
  facet_grid(. ~ job)

Facets - finding the best. Try Points

# Can we apply points between age and balance?
ggplot(bank, aes(age, balance, color = job)) +
  geom_point() + geom_smooth() +
  facet_grid(. ~ job)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Facets - finding the best. Try Smoothie

# Smooth line is mixed with points
ggplot(bank, aes(age, balance)) + geom_point(aes(color = job)) +
  geom_smooth() +
  facet_grid(. ~ job)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

With facets or without facets?

ggplot(bank, aes(previous)) + geom_histogram() + facet_grid(. ~ job)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

With facets or without facets?

facets with color

ggplot(bank, aes(previous)) + geom_histogram(aes(fill = job)) +
  facet_grid(. ~ job)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

With facets or without facets?

Use density/color on one figure

ggplot(bank, aes(previous)) + geom_density(aes(fill = job))

With facets or without facets: Case 2

ggplot(bank, aes(previous)) + geom_histogram() +
  facet_grid(. ~ marital)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

With facets or without facets: geom_density and fill

ggplot(bank, aes(previous)) + geom_density(aes(fill = marital))

With facets or without facets: xlim

ggplot(bank, aes(previous)) +
  geom_density(aes(fill = marital), alpha = 0.7) +
  xlim(1, 10)
## Warning: Removed 3725 rows containing non-finite values (stat_density).

Facets in full power

# Levels gives more control to the layer and style.
cutpoints <- quantile(bank$age, seq(0, 1, length = 4), na.rm = TRUE)
# The age_group variable is now a categorical factor variable containing
# 3 levels, indicating the ranges of age.
bank$age_group <- cut(bank$age, cutpoints)
levels(bank$age_group)
## [1] "(19,35]" "(35,45]" "(45,87]"
# Use facet_wrap to specify nrow/ncol.
ggplot(bank, aes(age, duration)) +
  geom_point(alpha = 1/3) + 
  facet_wrap(job ~ age_group, nrow = 2) +
  geom_smooth(method="lm", se=FALSE, col="steelblue") + 
  theme_bw(base_size = 10) + 
  labs(x = "age", y = expression("log " * Duration)) + 
  scale_y_log10() +
  labs(title = "Bank Clients")  

Facets in full power: plot

Add theme

default theme is theme_gray()

g <- ggplot(bank, aes(x = age, y = log10(duration)))
g + geom_point(aes(color = job), size = 4, alpha = 1/2) + theme_bw()
g + geom_point(aes(color = job), size = 4, alpha = 1/2) + theme_void()

g + geom_point(aes(color = job), size = 4, alpha = 1/2) + theme_minimal() +
    labs(title = "Duration is longer with age",
        subtitle = "some random plot",
        caption = "from MFE") +
    labs(x = "age", y = expression("log " * Duration))

Add theme: result

ggthemes

• package ggthemes provides many other themes.

library(ggthemes)
## [1] "theme_base" "theme_calc"
## [3] "theme_economist" "theme_economist_white"
## [5] "theme_excel" "theme_few"
## [7] "theme_fivethirtyeight" "theme_foundation"
## [9] "theme_gdocs" "theme_hc"
## [11] "theme_igray" "theme_map"
## [13] "theme_pander" "theme_par"
## [15] "theme_solarized" "theme_solarized_2"
## [17] "theme_solid" "theme_stata"
## [19] "theme_tufte" "theme_wsj"

ggplot summary

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(
     mapping = aes(<MAPPINGS>),
     stat = <STAT>, 
     position = <POSITION>
  ) +
  <COORDINATE_FUNCTION> +
  <FACET_FUNCTION>

ggplot: one more thing: nz

# install.packages("maps")
library(maps)
nz <- map_data("nz")
ggplot(nz, aes(long, lat, group = group)) +
  geom_polygon(fill = "white", colour = "black") +
  coord_quickmap()

ggplot: one more thing - world

world <- map_data("world")
ggplot(world, aes(long, lat, group = group)) +
  geom_polygon(fill = "white", colour = "black") +
  coord_quickmap()

Assignment

• In an R Markdown document (output: html), produce 10 data insights.
  • Each with a figure and a data story.
  • You can re-use about existing examples, up to 5.

Lecture 9: Shiny/3: Advanced

Review: Display output with render*() functions

• render*() arguments are code used to build and rebuild object
• render*() function re-runs the code with every change in the input

Review: render/output pair

• Static table from df, mat, etc.s
  • renderTable()/tableOutput()
• Interactive table from data frame, matrix or other table-like structure
  • renderDataTable()/dataTableOutput()
• Plot
  • renderPlot()/plotOutput()
• Get continous output
  • renderPrint()/verbatimTextOutput() or textOutput()
• Get last result
  • renderText()verbatimTextOutput() or textOutput()
• Customized UI elements
  • uiOutput()/renderUI()

Review: render*

• Allow binding of one output to multiple inputs

  output$hist <- renderPlot({
    hist(data())
  })
  
  output$stat <- renderPlot({
    summary(data())
  })

Review: observeEvent

• Allow binding of multiple outputs to multiple inputs.

• Use of isolate to peek the value not to react to its change every time.

  actionButton(inputId = "go", label = "Click me")
  
  observeEvent(input$go, {
    # Use of isolate to *peek* the value not to react to it.
    num_input <- isolate(input$num_input)
  
    output$plot1 <- renderPlot({ 
      # if we use input$num_input here, we build a direct reactive link
      # between output$plot1 and input$num_input. This is not what we designed.
      plot(1:number_input, runif(num_input))
    })
  
    output$table1 <- renderTable({ ... })
  })

Review: When code gets to run.

• ui: client. run once per user per session.
• server: run once per session
• code inside a reactive function runs with every input change.

Advanced Shiny

• Input: update***Input functions (You can update output with new content.)
• Dynamic UI to create inputs and outputs dynamically.
• Graphics output: gridExtra
• Table output: kableExtra

update***Input

• Update various input values
  • updateSelectionInput(…)
  • updateNumericInput(…)

# shiny-36-update.R

library(shiny)

ui <- fluidPage(
  numericInput("shock", "Shock", value = round(runif(1) * 1000), 0),
  actionButton("add", "Add"),
  checkboxGroupInput("scenarios", "Scenarios", choices = c(), selected = c()),

  verbatimTextOutput("o1")
)

scenarios <- c(-100, -50, 0, 50, 100)

server <- function(input, output, session) {
  updateCheckboxGroupInput(session, "scenarios",
                           choices = scenarios,
                           selected = scenarios)  

  observeEvent(input$add, {
    
    shock <- isolate(input$shock)
    
    if (!(shock %in% scenarios)) {
      scenarios <<- sort(c(scenarios, shock))
      updateCheckboxGroupInput(session, "scenarios",
                               choices = scenarios,
                               selected = scenarios)
    }
    # put a new random value
    updateNumericInput(session, "shock", value = round(runif(1) * 1000))
  })
  
  output$o1 <- renderPrint({
    x <- input$scenarios
    str(x)
    cat(paste0("length: ", length(x), "\n"))
    cat(paste0(x, "\n"))
  })
}

shinyApp(ui, server)

renderUI

• Dynamicaly creation of UI (user interface) with input and outputs.
• Append new items to tagList()

Create dynamic output tagList()

# shiny-34-renderUI.R

library(shiny)

ui <- fluidPage(
  uiOutput("p1")
)

server <- function(input, output, session) {
  output$p1 <- renderUI({
    uiopt <- tagList()
    
    for (i in 1:3) {
      uiopt <- tagList(
        uiopt,
        h1(paste0("HTML t", i)),
        h1(paste0("Plot p", i)),
        plotOutput(paste0("plot", i)))
    }

    uiopt                     
  })
  
  output[[paste0("plot", 1)]] <- renderPlot({ hist(rnorm(1000, 0, 1)) })
  output$plot2 <- renderPlot({ hist(runif(1000)) })
  
  for (i in 1:3) {
    output[[paste0("plot", i)]] <- renderPlot({ hist(rnorm(1000, 0, i)) })
  }
}

shinyApp(ui, server)

Create dynamic output 2

You can use newly created UI immediately

# shiny-35-renderUI-2.R

library(shiny)

ui <- fluidPage(
  uiOutput("p1")
)

server <- function(input, output, session) {
  output$p1 <- renderUI({
    tl <- tagList(
      h1("HTML t1"),
      uiOutput("t1"),
      h1("Plot p1p1"),
      plotOutput("p1p1")
    )

    tl
  })
  
  output$t1 <- renderUI({
    tagList(
      h1("HTML p1t1 inside t1"),
      plotOutput("p1t1")
    )
  })

  output$p1t1 <- renderPlot({
    # hist(runif(10000))
    plot(1:100, runif(100))
  })
  
  output$p1p1 <- renderPlot({
    plot(1:100, runif(100))
  })
  
  
}

shinyApp(ui, server)

Create dynamic output 3

# shiny-32-renderUI.R

library(shiny)
library(knitr)
library(kableExtra)

ui <- fluidPage(
  numericInput("num", "Num", 3),
  uiOutput("p1"),
  hr(),
  tableOutput("p2")
)

server <- function(input, output, session) {
  observe({
    row_num <- input$num

    output$p1 <- renderUI({
      tagList(
        tags$h1("This is a header"),
        {
          if (row_num > 0 & row_num < 7) {
            hx <- paste0("h", row_num)
            (tags[[hx]])(toupper(hx))
          } else {
            (tags[["h6"]])(toupper("h6"))
          }
        },
        numericInput("num_plot", "Give a number",
          value = round(runif(1, min = 0, max = nrow(iris)), 0),
          min = 0, max = nrow(iris)),
        plotOutput("plot"),
        
        tags$h3("kable can't be used with tagList."),
        kable(iris[1:row_num, , drop = T], format = "html")
      )
    })
    
    # num_plot is the newly created input.
    # plot is the newly created output.
    # You can use the newly created input/output immediately
    # This is particularly useful for creating multiple plots and tables.
    output$plot <- renderPlot({
      if (input$num_plot > 0) {
        ggplot(iris[1:input$num_plot, , drop = F],
               aes(x = Sepal.Length, y = Petal.Width)) +
          geom_point() +
          geom_smooth() +
          theme_minimal()
      }
    })

    # Use anything together with kable, use function() { paste0(...) }
    output$p2 <- function() {
      paste0(
        tags$h1("kable is used inside a function()"),
        kable(iris[1:row_num, , drop = T], format = "html"))
    }
  })
}

shinyApp(ui, server)

Dynamic input and update***Input

• Update various input values
  • updateSelectionInput(…)
  • updateNumericInput(…)

# shiny-36-update.R

library(shiny)

ui <- fluidPage(
  numericInput("shock", "Shock", value = round(runif(1) * 1000), 0),
  actionButton("add", "Add"),
  checkboxGroupInput("scenarios", "Scenarios", choices = c(), selected = c()),

  verbatimTextOutput("o1")
)

scenarios <- c(-100, -50, 0, 50, 100)

server <- function(input, output, session) {
  updateCheckboxGroupInput(session, "scenarios",
                           choices = scenarios,
                           selected = scenarios)  

  observeEvent(input$add, {
    
    shock <- isolate(input$shock)
    
    if (!(shock %in% scenarios)) {
      scenarios <<- sort(c(scenarios, shock))
      updateCheckboxGroupInput(session, "scenarios",
                               choices = scenarios,
                               selected = scenarios)
    }
    # put a new random value
    updateNumericInput(session, "shock", value = round(runif(1) * 1000))
  })
  
  output$o1 <- renderPrint({
    x <- input$scenarios
    str(x)
    cat(paste0("length: ", length(x), "\n"))
    cat(paste0(x, "\n"))
  })
}

shinyApp(ui, server)

Very dynamic

# shiny-37-createDynamic.R

library(shiny)

ui <- fluidPage(
  uiOutput("p1"),
  verbatimTextOutput("o1")
)

scenarios <- c(-100, -50, 0, 50, 100)

server <- function(input, output, session) {
  baseList <- tagList(
    numericInput("shock", "Shock", value = round(runif(1) * 1000), 0),
    actionButton("add", "Add")
  )

  gen_ui <- function(scenarios, values = NA) {
    output$p1 <- renderUI({
      tl <<- baseList
      # print(scenarios)
      for (ss in 1:length(scenarios)) {
        nm <- paste0("scenarios_", ss)
        if (is.na(values[ss])) {
          val <- TRUE 
        } else {
          val <- values[ss]
        }
        tl <- tagList(tl, checkboxInput(nm, scenarios[ss], value = val))
        # print(paste0("scenarios_", ss, ", ", scenarios[ss], "\n"))
      }
      tl
    })
  }

  gen_ui(scenarios)

  observeEvent(input$add, {
    shock <- isolate(input$shock)
    if (!(shock %in% scenarios)) {
      vals <- purrr::map_lgl(1:length(scenarios),
                       function(ss) {
                         isolate(input[[paste0("scenarios_", ss)]])
                       })
      scenarios <<- c(scenarios, shock)
      gen_ui(scenarios, vals)
    }

    # put a new random value
    updateNumericInput(session, "shock", value = round(runif(1) * 1000))
  })
  
  output$o1 <- renderPrint({
    print(names(input))
    print(isolate(input[["scenarios_1"]]))
    x <- purrr::keep(1:length(scenarios),
                     function(ss) {
                       isolate(input[[paste0("scenarios_", ss)]])
                      })
    x <- scenarios[x]
    str(x)
    cat(paste0("length: ", length(x), "\n"))
    cat(paste0(x, "\n"))
  })
}

shinyApp(ui, server)

ggplot/gridExtra

If we need to generate multiple plots. ggplot has a companion package to arrange plots.

SxS: side by side

library(gridExtra)
p1 <- ggplot(bank) + geom_bar(mapping = aes(x = age, fill = job),
                              position = "fill") + coord_polar()
p2 <- ggplot(bank) + geom_bar(mapping = aes(x = age, fill = education),
                              position = "fill") + coord_polar()

grid.arrange(p1, p2, ncol=2, nrow=1)

grid.arrange(p1, p2, ncol=2, nrow=1, widths = c(4,3))

ggplot/gridExtra

library(tibble)
library(ggplot2)
library(gridExtra)

df <- tibble(x = rnorm(1000), y = rnorm(1000))

hist_top <- ggplot(df, aes(x = x)) + geom_density()

empty <-
  ggplot()+geom_point(aes(1,1), colour="white")+
  theme(axis.ticks=element_blank(), 
        panel.background=element_blank(), 
        axis.text.x=element_blank(), axis.text.y=element_blank(),
        axis.title.x=element_blank(), axis.title.y=element_blank())

scatter <- ggplot(df, aes(x = x, y = y)) + geom_point()

hist_right <- ggplot(df, aes(x = y)) + geom_density() + coord_flip()

grid.arrange(hist_top, empty, scatter, hist_right,
             ncol=2, nrow=2,
             widths=c(3.5, 0.7), heights=c(1, 4))

ggplot/gridExtra: result

knitr/kableExtra

kable is provided by knitr package. kableExtra enhance it with more functions. So we load both packages.

```{r shiny_block}
library(knitr)
library(kableExtra)

# This is HTML output
kable(df, format = "html")

# Use function() { } to output html
output$p1 <- function() {
  kable(df, format = "html")
}
```

kable_styling

• Get all styles from here https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html

• style

mtcars[1:10, , drop = F] %>%
kable("html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                font_size = 12,
                full_width = F, # True for left-to-right width
                position = "left") # if full_width == F

kable_styling: column_spec

mtcars[1:10, , drop = F] %>%
kable("html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                font_size = 12,
                full_width = F, # True for left-to-right width
                position = "left") %>% # if full_width == FALSE
    column_spec(1, bold = T, border_right = TRUE) %>%
    column_spec(2, width = "30em", background = "yellow")

kable_styling: row_spec

mtcars[1:10, , drop = F] %>%
kable("html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                font_size = 12,
                full_width = F, # True for left-to-right width
                position = "left") %>% # if full_width == F
    column_spec(5:7, bold = TRUE) %>%
    row_spec(3:5, bold = T, color = "white", background = "#D7261E")    

kable_styling: cell_spec